{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-21T02:37:13.065450Z",
     "start_time": "2025-03-21T02:37:13.049548Z"
    }
   },
   "source": [
    "import gym\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ch17.Ensemble import *\n",
    "from ch17.SAC_Continuous import SAC"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T02:38:02.807132Z",
     "start_time": "2025-03-21T02:38:02.775362Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class MBPO:\n",
    "    def __init__(self,env,agent,fake_env,env_pool,model_pool,rollout_length,rollout_batch_size,real_ratio,num_episode):\n",
    "        self.env = env\n",
    "        self.agent = agent\n",
    "        self.fake_env = fake_env\n",
    "        self.env_pool = env_pool\n",
    "        self.model_pool = model_pool\n",
    "        self.rollout_length = rollout_length\n",
    "        self.rollout_batch_size = rollout_batch_size\n",
    "        self.real_ratio = real_ratio\n",
    "        self.num_episode = num_episode\n",
    "\n",
    "    def rollout_model(self):\n",
    "        observations,_,_,_,_ = self.env_pool.sample(self.rollout_batch_size)\n",
    "        for obs in observations:\n",
    "            for i in range(self.rollout_length):\n",
    "                action = self.agent.take_action(obs)\n",
    "                reward,next_obs = self.fake_env.step(obs,action)\n",
    "                self.model_pool.add(obs,action,reward,next_obs,False)\n",
    "                obs = next_obs\n",
    "\n",
    "    def update_agent(self,policy_train_batch_size=64):\n",
    "        env_batch_size = int(policy_train_batch_size * self.real_ratio)\n",
    "        model_batch_size = policy_train_batch_size - env_batch_size\n",
    "        for epoch in range(10):\n",
    "            env_obs,env_action,env_reward,env_next_obs,env_done = self.env_pool.sample(env_batch_size)\n",
    "            if self.model_pool.size() >0 :\n",
    "                model_obs,model_action,model_reward,model_next_obs,model_done = self.model_pool.sample(model_batch_size)\n",
    "                obs = np.concatenate((env_obs,model_obs),axis=0)\n",
    "                action = np.concatenate((env_action,model_action),axis=0)\n",
    "                reward = np.concatenate((env_reward,model_reward),axis=0)\n",
    "                next_obs = np.concatenate((env_next_obs,model_next_obs),axis=0)\n",
    "                done = np.concatenate((env_done,model_done),axis=0)\n",
    "            else:\n",
    "                obs,action,next_obs,reward,done = env_obs,env_action,env_next_obs,env_reward,env_done\n",
    "            transition_dict = {'states':obs,'actions':action,'rewards':reward,'next_states':next_obs,'dones':done}\n",
    "            self.agent.update(transition_dict)\n",
    "\n",
    "    def train_model(self):\n",
    "        obs,action,reward,next_obs,done = self.env_pool.return_all_samples()\n",
    "        inputs = np.concatenate((obs,action),axis=-1)\n",
    "        reward = np.array(reward)\n",
    "        labels = np.concatenate((np.reshape(reward,(reward.shape[0],-1)),next_obs-obs),axis=-1)\n",
    "        self.fake_env.model.train(inputs,labels)\n",
    "\n",
    "    def explore(self):\n",
    "        obs,done,episode_return = self.env.reset(),False,0\n",
    "        while not done:\n",
    "            action = self.agent.take_action(obs)\n",
    "            next_obs,reward,done,_ = self.env.step(action)\n",
    "            self.env_pool.add(obs,action,reward,next_obs,done)\n",
    "            obs = next_obs\n",
    "            episode_return += reward\n",
    "        return episode_return\n",
    "\n",
    "    def train(self):\n",
    "        return_list = []\n",
    "        explore_return = self.explore()  # 先进行随机策略的探索来收集第一条序列的数据\n",
    "        print('episode: 1, return: %d' % explore_return)\n",
    "        return_list.append(explore_return)\n",
    "\n",
    "        for i_episode in range(self.num_episode-1):\n",
    "            obs,done,episode_return = self.env.reset(),False,0\n",
    "            step = 0\n",
    "            while not done:\n",
    "                if step % 50 == 0:\n",
    "                    self.train_model()\n",
    "                    self.rollout_model()\n",
    "                action = self.agent.take_action(obs)\n",
    "                next_obs,reward,done,_ = self.env.step(action)\n",
    "                self.env_pool.add(obs,action,reward,next_obs,done)\n",
    "                obs = next_obs\n",
    "                episode_return += reward\n",
    "\n",
    "                self.update_agent()\n",
    "                step += 1\n",
    "\n",
    "            return_list.append(episode_return)\n",
    "            print('episode: %d, return: %d' % (i_episode+2, episode_return))\n",
    "        return return_list"
   ],
   "id": "7775fec1c6297fd8",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T02:44:11.442497Z",
     "start_time": "2025-03-21T02:38:04.846380Z"
    }
   },
   "cell_type": "code",
   "source": [
    "real_ration = 0.5\n",
    "env_name = 'Pendulum-v0'\n",
    "env = gym.make(env_name)\n",
    "num_episodes = 20\n",
    "actor_lr = 5e-4\n",
    "critic_lr = 5e-3\n",
    "alpha_lr = 1e-3\n",
    "hidden_dim = 128\n",
    "gamma = 0.98\n",
    "tau = 0.005\n",
    "buffer_size=10000\n",
    "target_entropy = -1\n",
    "model_alpha = 0.01  # 模型损失函数中的加权权重\n",
    "\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.shape[0]\n",
    "action_bound = env.action_space.high[0]\n",
    "\n",
    "rollout_batch_size = 1000\n",
    "rollout_length = 1  # 推演长度k，推荐更多尝试\n",
    "model_pool_size = rollout_batch_size * rollout_length\n",
    "\n",
    "agent = SAC(state_dim,hidden_dim,action_dim,action_bound,actor_lr,critic_lr,alpha_lr,target_entropy,tau,gamma,device)\n",
    "model = EnsembleDynamicsModel(state_dim,action_dim,model_alpha)\n",
    "fake_env = FakeEnv(model)\n",
    "env_pool = ReplayBuffer(buffer_size)\n",
    "model_pool = ReplayBuffer(model_pool_size)\n",
    "\n",
    "mbpo = MBPO(env,agent,fake_env,env_pool,model_pool,rollout_length,rollout_batch_size,real_ration,num_episodes)\n",
    "\n",
    "return_list = mbpo.train()\n",
    "\n",
    "episode_list = list(range(len(return_list)))\n",
    "plt.plot(episode_list,return_list)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('MBPO on {}'.format(env_name))\n",
    "plt.show()"
   ],
   "id": "3055c3894869212f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 1, return: -1189\n",
      "episode: 2, return: -1617\n",
      "episode: 3, return: -1357\n",
      "episode: 4, return: -1592\n",
      "episode: 5, return: -627\n",
      "episode: 6, return: -385\n",
      "episode: 7, return: -1498\n",
      "episode: 8, return: -148\n",
      "episode: 9, return: -1515\n",
      "episode: 10, return: -1499\n",
      "episode: 11, return: -1493\n",
      "episode: 12, return: -129\n",
      "episode: 13, return: -1044\n",
      "episode: 14, return: -126\n",
      "episode: 15, return: -245\n",
      "episode: 16, return: -1326\n",
      "episode: 17, return: -120\n",
      "episode: 18, return: -123\n",
      "episode: 19, return: -250\n",
      "episode: 20, return: -1484\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-21T02:37:15.745367Z",
     "start_time": "2025-03-21T02:37:15.729623Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "8db9ae41aab63db4",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "2cc3b3929117d4e6"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
